We aim to design an RL-based agent that is able to conduct automatic task decomposition and action pruning in solving text-based games.
We consider games sharing similar themes and tasks, but varying in their complexities [1]}, [2]}.
Taking the cooking games [3]} as an example, the task is always “make the meal”. To accomplish this task, the agent has to explore different rooms to collect all ingredients, prepare them in right ways, and make the meal.
A game's complexity depends on the number of rooms, ingredients, and the required preparation steps.
We define a subtask as a milestone towards completing the task (e.g., “get apple” if “apple” is included in the recipe), and a subtask requires a sequence of actions to accomplish (e.g., the agent has to explore the house to find the apple).
A game is considered simple, if it consists of only a few subtasks, and complex if it consists of more subtasks.
Fig. REF  gives examples of simple games and complex games.
While being closer to real world applications, complex games are hard to solve by RL agents because:
1) it's expensive to collect sufficient human labeled data for pre-training;
2) it's unrealistic to train an RL agent from scratch.
We therefore focus on agent's sample efficiency and performance on complex games. Our objective is to leverage the labeled data collected from simple games to speed up RL training in complex games, thus obtaining an agent capable of complex games.
For more details and statistics of the simple / complex games used in our work, please refer to Sec. REF .
